mclass-sky

Multiclass Active Learning Algorithms with Application in Astronomy.

Contributors:	Alasdair Tran, Cheng Soon Ong, Jakub Nabaglo, David Wu, Wei Yen Lee
License:	This package is distributed under a a 3-clause ("Simplified" or "New") BSD license.
Source:	https://github.com/chengsoonong/mclass-sky
Doc:	https://mclearn.readthedocs.io/en/latest/
Publications:	Combining Active Learning Suggestions by Alasdair Tran, Cheng Soon Ong, and Christian Wolf Active Learning with Gaussian Processes by Jakub Nabaglo Photometric Classification with Thompson Sampling by Alasdair Tran Cutting-Plane Methods with Active Learning by David Wu

Introduction

This repository contains a collection of projects related to active learning methods with application in astronomy. Click on one of the links below to go to the directory of a particular project.

1. Combining Active Learning Suggestions by Alasdair Tran, Cheng Soon Ong, and Christian Wolf
2. Active Learning with Gaussian Processes for Photometric Redshift Prediction
3. Cutting-plane Methods with Applications in Convex Optimization and Active Learning
4. Photometric Classification with Thompson Sampling

mclearn

mclearn is a Python package that implement selected multiclass active learning algorithms, with a focus in astronomical data.

The dependencies are Python 3.4, numpy, pandas, matplotlib, seaborn, ephem, scipy, ipython, and scikit-learn. It's best to first install the Anaconda distribution for Python 3, then install mclearn using pip:

pip install mclearn